Developing a Methodology of Structuring and Layering Technological Information in Patent Documents through Natural Language Processing

Roh, Taeyeoun; Jeong, Yujin; Yoon, Byungun

doi:10.3390/su9112117

Cited by 19 publications

(8 citation statements)

References 43 publications

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“…An inventive design method is extracted, based on TRIZ and patent semantic similarity [40]. Furthermore, technology information is structured in patent documents, using their linguistic character and patent law [41].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Technology opportunity discovery by structuring user needs based on natural language processing and machine learning

et al. 2019

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Discovering technology opportunities from the opinion of users can promote successful technological development by satisfying the needs of users. However, although previous approaches using opinion mining only have classified various needs of users into positive or negative categories, they cannot derive the main reasons for their opinion. To solve this problem, this research proposes an approach to exploring technology opportunity by structuring user needs with a concept of opinion trigger of objects and functions of the technology-based products. To discover technology opportunity, first, an opinion trigger is identified from review data using Naïve Base classifier and natural language processing. Second, the opinion triggers and patent keywords that have a similar meaning in context are clustered to discover the needs of the user and need-related technology. Then, the sentimental values of needs are calculated through graph-based semi-supervised learning. Finally, the needs of the user are classified in resolving the problem of vacant technology to discover technology opportunity. Then, an R&D strategy of each opportunity is suggested based on opinion triggers, patent keywords, and their property. Based on the concept of opinion trigger-based methodology, a case study is conducted on automobile—related reviews, extracting the customer needs and presenting important R&D projects such as an extracted need (cargo transportation) and its R&D strategy (resolving contradiction). The proposed approach can analyze the needs of user at a functional level to discover new technology opportunities.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Technology opportunity discovery by structuring user needs based on natural language processing and machine learning

et al. 2019

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“…Kim et al [22] have used text-mining patent keyword extraction for sustainable technology management. Roh et al [23] have developed a methodology for structuring and layering technological information to apply text mining to patent documents. Table 1.…”

Section: Text Miningmentioning

confidence: 99%

“…The simple structure and ease of use of this method have enabled various applications of patent text analysis [23,37].…”

Section: Document Similarity Calculation Modulementioning

confidence: 99%

Constructing Patent Maps Using Text Mining to Sustainably Detect Potential Technological Opportunities

Wang¹,

Chang²,

Hsin³

2018

Sustainability

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With the advent of the knowledge economy, firms often compete for intellectual property rights. Being the first to acquire high-potential patents can assist firms in achieving future competitive advantages. To identify patents capable of being developed, firms often search for a focus by using existing patent documents. Because of the rapid development of technology, the number of patent documents is immense. A prominent topic among current firms is how to use this large number of patent documents to discover new business opportunities while avoiding conflicts with existing patents. In the search for technological opportunities, a crucial task is to present results in the form of an easily understood visualization. Currently, natural language processing can help in achieving this goal. In natural language processing, word sense disambiguation (WSD) is the problem of determining which “sense” (meaning) of a word is activated in a given context. Given a word and its possible senses, as defined by a dictionary, we classify the occurrence of a word in context into one or more of its sense classes. The features of the context (such as neighboring words) provide evidence for these classifications. The current method for patent document analysis warrants improvement in areas, such as the analysis of many dimensions and the development of recommendation methods. This study proposes a visualization method that supports semantics, reduces the number of dimensions formed by terms, and can easily be understood by users. Since polysemous words occur frequently in patent documents, we also propose a WSD method to decrease the calculated degrees of distortion between terms. An analysis of outlier distributions is used to construct a patent map capable of distinguishing similar patents. During the development of new strategies, the constructed patent map can assist firms in understanding patent distributions in commercial areas, thereby preventing patent infringement caused by the development of similar technologies. Subsequently, technological opportunities can be recommended according to the patent map, aiding firms in assessing relevant patents in commercial areas early and sustainably achieving future competitive advantages.

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“…Some works focus on patent text representations, e.g., in order to identify a technical patent topic from a patent abstract; the authors of [2] combined semantic role labeling (SRL) information with framework rules of semantics, which improves the performance in SRL systems by obtaining knowledge from patents. Some works focus on the extraction of semantic features from patent texts, e.g., the authors of [3] extracted keywords related to technical information from patent documents, constructed keyword collections, and obtained the layer of keywords according to the level of information. Additionally, machine learning has drawn more attention for the PAC system, e.g., the authors of [4] developed an automatic patent quality analysis and classification system SOM-KPCA-SVM, using data mining methods to identify and classify the quality of new patents in a timely manner.…”

Section: Introductionmentioning

confidence: 99%

Patent Automatic Classification Based on Symmetric Hierarchical Convolution Neural Network

Zhu

Fang

et al. 2020

Symmetry

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With the rapid growth of patent applications, it has become an urgent problem to automatically classify the accepted patent application documents accurately and quickly. Most previous patent automatic classification studies are based on feature engineering and traditional machine learning methods like SVM, and some even rely on the knowledge of domain experts, hence they suffer from low accuracy problem and have poor generalization ability. In this paper, we propose a patent automatic classification method via the symmetric hierarchical convolution neural network (CNN) named PAC-HCNN. We use the title and abstract of the patent as the input data, and then apply the word embedding technique to segment and vectorize the input data. Then we design a symmetric hierarchical CNN framework to classify the patents based on the word embeddings, which is much more efficient than traditional RNN models dealing with texts, meanwhile keeping the history and future information of the input sequence. We also add gated linear units (GLUs) and residual connection to help realize the deep CNN. Additionally, we equip our model with a self attention mechanism to address the long-term dependency problem. Experiments are performed on large-scale datasets for Chinese short text patent classification. Experimental results prove our proposed model’s effectiveness, and it performs better than other state-of-the-art models significantly and consistently on both fine-grained and coarse-grained classification.

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Developing a Methodology of Structuring and Layering Technological Information in Patent Documents through Natural Language Processing

Cited by 19 publications

References 43 publications

Technology opportunity discovery by structuring user needs based on natural language processing and machine learning

Technology opportunity discovery by structuring user needs based on natural language processing and machine learning

Constructing Patent Maps Using Text Mining to Sustainably Detect Potential Technological Opportunities

Patent Automatic Classification Based on Symmetric Hierarchical Convolution Neural Network

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